With increasing energy costs, there is a growing interest in energy monitoring. For instance, with the advent of demand-response pricing in which the price of electricity at the entry point to a building can fluctuate instantaneously, knowing the present power consumption and the allocation of power among the various devices and systems powered can be beneficial in optimizing energy cost.
Knowledge of whether the present rate of energy consumption is optimal or reasonable for the present conditions can also be beneficial. In some cases, whether these optimal conditions exist is relatively easy to determine. For example, when a room is totally unoccupied, it is reasonable to turn un-needed lights off. Similarly, in a home environment, leaving an electric oven “on” in the hot summer when no one is cooking is not normally a reasonable practice. By contrast, the optimal or appropriate operation of more complex appliances or equipment is less easy to determine.
As an example, undetected refrigerant loss in Vapor Compression Cycle (VCC) equipment, or a so-called heat pumping system that removes heat from one space and deposits in another, such as a residential or commercial heat pump, air conditioning or refrigeration system, can be a significant source of annoyance and cause of excessive and wasteful energy usage. Most refrigerant leakage losses are not fast enough to readily detect the degradation in performance of the unit over the course of a day or even a week. In cases in which VCC equipment is used strictly as an air conditioner, refrigerant loss can occur over the winter while the system is idle. When an air conditioning system is first turned on or activated in the spring, system usage is generally relatively low and a loss of efficiency due to refrigerant loss can go undetected, manifesting itself only when system usage increases on hotter days. In a residential split system that includes an outdoor compressor/condenser unit and an indoor evaporator/air handler unit, the compressor is located outside the residence, and the residents of a dwelling may not notice a problem until either an unexpectedly large bill is received from the utility or the capacity of the air conditioning system is degraded to the point where it cannot keep up with demand. In either case, frustration can result as many residences in a geographical region discover the problem simultaneously on a hot day, and it becomes challenging and time-consuming to dispatch technicians to diagnose and remedy this common problem. This problem extends to commercial systems as well. A method that can reliably and quickly detect and report abnormalities such as a loss of refrigerant would be highly desirable.
With the recent advent of higher energy prices, there is becoming increased interest in power and energy monitoring. Applied to an HVAC system, it is not sufficient to know merely how much energy is consumed, although this is useful information. More importantly, it would be useful to be able to predict whether the HVAC system is operating normally for the ambient conditions encountered, including the outdoor temperature and the conditions in the space for which temperature control is provided.
The expected normal operation of an HVAC system is not always intuitively apparent. First, there can be unit-to-unit manufacturing variations, including normal manufacturing tolerances, causing variation in compressor isentropic efficiency, condenser and evaporator efficiency, and other aspects. More importantly, no two systems are installed in precisely the same manner, resulting in different air flows across the condenser and evaporator coils from unit to unit, different lengths of refrigerant lines in split-system applications, and varying efficiency of refrigerant line insulation. Additionally, the system is highly sensitive to the level to which it is charged with refrigerant, and there is significant variance from unit to unit and from charging to charging that makes it very difficult to determine a-priori the power consumption of a system.
It would be desirable to provide a system and method that can automatically learn to predict the expected behavior of VCC-based equipment, and subsequently detect and report such conditions as refrigerant loss in a timely manner, without needing to disturb the vapor compression equipment in any way. The present disclosure is directed to such a system and method.